import copy
import inspect
import unittest

import numpy as np
from huggingface_hub import hf_hub_download

from transformers import VideoMAEConfig
from transformers.models.auto import get_values
from transformers.testing_utils import require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
				import torch
				from torch import nn

				from transformers import (
				    MODEL_FOR_VIDEO_CLASSIFICATION_MAPPING,
				    VideoMAEForPreTraining,
				    VideoMAEForVideoClassification,
				    VideoMAEModel,
				)
				from transformers.models.videomae.modeling_videomae import VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST


if is_vision_available():
				from transformers import VideoMAEImageProcessor



class   lowercase_       :



							def __init__(		self ,							lowercase_ ,							lowercase_=13 ,							lowercase_=10 ,							lowercase_=3 ,							lowercase_=2 ,							lowercase_=2 ,							lowercase_=2 ,							lowercase_=True ,							lowercase_=True ,							lowercase_=32 ,							lowercase_=5 ,							lowercase_=4 ,							lowercase_=37 ,							lowercase_="gelu" ,							lowercase_=0.1 ,							lowercase_=0.1 ,							lowercase_=10 ,							lowercase_=0.02 ,							lowercase_=0.9 ,							lowercase_=None ,							):
													_snake_case		:	Tuple							=       parent
													_snake_case		:	Union[str, Any]							=       batch_size
													_snake_case		:	List[Any]							=       image_size
													_snake_case		:	str							=       num_channels
													_snake_case		:	Optional[Any]							=       patch_size
													_snake_case		:	Optional[Any]							=       tubelet_size
													_snake_case		:	List[Any]							=       num_frames
													_snake_case		:	List[Any]							=       is_training
													_snake_case		:	Tuple							=       use_labels
													_snake_case		:	Union[str, Any]							=       hidden_size
													_snake_case		:	Any							=       num_hidden_layers
													_snake_case		:	Dict							=       num_attention_heads
													_snake_case		:	int							=       intermediate_size
													_snake_case		:	Any							=       hidden_act
													_snake_case		:	str							=       hidden_dropout_prob
													_snake_case		:	List[str]							=       attention_probs_dropout_prob
													_snake_case		:	int							=       type_sequence_label_size
													_snake_case		:	Tuple							=       initializer_range
													_snake_case		:	Any							=       mask_ratio
													_snake_case		:	int							=       scope

													# in VideoMAE, the number of tokens equals num_frames/tubelet_size * num_patches per frame
													_snake_case		:	Tuple							=       (image_size // patch_size) ** 2
													_snake_case		:	Optional[Any]							=       (num_frames // tubelet_size) * self.num_patches_per_frame

													# use this variable to define bool_masked_pos
													_snake_case		:	Union[str, Any]							=       int(mask_ratio * self.seq_length							)



							def 			UpperCamelCase			(		self							):
													_snake_case		:	Dict							=       floats_tensor(
													    [self.batch_size, self.num_frames, self.num_channels, self.image_size, self.image_size]							)

													_snake_case		:	Optional[int]							=       None
													if self.use_labels:
																			_snake_case		:	Tuple							=       ids_tensor([self.batch_size] ,							self.type_sequence_label_size							)

													_snake_case		:	Any							=       self.get_config()

													return config, pixel_values, labels



							def 			UpperCamelCase			(		self							):
													return VideoMAEConfig(
													    image_size=self.image_size ,							patch_size=self.patch_size ,							num_channels=self.num_channels ,							num_frames=self.num_frames ,							tubelet_size=self.tubelet_size ,							hidden_size=self.hidden_size ,							num_hidden_layers=self.num_hidden_layers ,							num_attention_heads=self.num_attention_heads ,							intermediate_size=self.intermediate_size ,							hidden_act=self.hidden_act ,							hidden_dropout_prob=self.hidden_dropout_prob ,							attention_probs_dropout_prob=self.attention_probs_dropout_prob ,							is_decoder=UpperCamelCase_ ,							initializer_range=self.initializer_range ,							)



							def 			UpperCamelCase			(		self ,							lowercase_ ,							lowercase_ ,							lowercase_							):
													_snake_case		:	Dict							=       VideoMAEModel(config=UpperCamelCase_							)
													model.to(UpperCamelCase_							)
													model.eval()
													_snake_case		:	Optional[Any]							=       model(UpperCamelCase_							)
													self.parent.assertEqual(result.last_hidden_state.shape ,							(self.batch_size, self.seq_length, self.hidden_size)							)



							def 			UpperCamelCase			(		self ,							lowercase_ ,							lowercase_ ,							lowercase_							):
													_snake_case		:	Any							=       VideoMAEForPreTraining(UpperCamelCase_							)
													model.to(UpperCamelCase_							)
													model.eval()
													# important: each video needs to have the same number of masked patches
													# hence we define a single mask, which we then repeat for each example in the batch
													_snake_case		:	int							=       torch.ones((self.num_masks,)							)
													_snake_case		:	List[str]							=       torch.cat([mask, torch.zeros(self.seq_length - mask.size(0							)							)]							)
													_snake_case		:	Tuple							=       mask.expand(self.batch_size ,							-1							).bool()

													_snake_case		:	Union[str, Any]							=       model(UpperCamelCase_ ,							UpperCamelCase_							)
													# model only returns predictions for masked patches
													_snake_case		:	Optional[Any]							=       mask.sum().item()
													_snake_case		:	str							=       3 * self.tubelet_size * self.patch_size**2
													self.parent.assertEqual(result.logits.shape ,							(self.batch_size, num_masked_patches, decoder_num_labels)							)



							def 			UpperCamelCase			(		self							):
													_snake_case		:	int							=       self.prepare_config_and_inputs()
													_snake_case							,_snake_case							,_snake_case		:	str							=       config_and_inputs
													_snake_case		:	int							=       {"pixel_values": pixel_values}
													return config, inputs_dict




@require_torch
class   lowercase_       (	a_		,					a_		,					unittest.TestCase						):
							_lowerCamelCase		=						(
							    (VideoMAEModel, VideoMAEForPreTraining, VideoMAEForVideoClassification) if is_torch_available() else ()
							)
							_lowerCamelCase		=						(
							    {'feature-extraction': VideoMAEModel, 'video-classification': VideoMAEForVideoClassification}
							    if is_torch_available()
							    else {}
							)

							_lowerCamelCase		=						False
							_lowerCamelCase		=						False
							_lowerCamelCase		=						False
							_lowerCamelCase		=						False



							def 			UpperCamelCase			(		self							):
													_snake_case		:	Optional[Any]							=       VideoMAEModelTester(self							)
													_snake_case		:	int							=       ConfigTester(self ,							config_class=UpperCamelCase_ ,							has_text_modality=UpperCamelCase_ ,							hidden_size=37							)



							def 			UpperCamelCase			(		self ,							lowercase_ ,							lowercase_ ,							lowercase_=False							):
													_snake_case		:	List[str]							=       copy.deepcopy(UpperCamelCase_							)

													if model_class == VideoMAEForPreTraining:
																			# important: each video needs to have the same number of masked patches
																			# hence we define a single mask, which we then repeat for each example in the batch
																			_snake_case		:	Dict							=       torch.ones((self.model_tester.num_masks,)							)
																			_snake_case		:	Any							=       torch.cat([mask, torch.zeros(self.model_tester.seq_length - mask.size(0							)							)]							)
																			_snake_case		:	Optional[int]							=       mask.expand(self.model_tester.batch_size ,							-1							).bool()
																			_snake_case		:	Optional[Any]							=       bool_masked_pos.to(UpperCamelCase_							)

													if return_labels:
																			if model_class in [
																			    *get_values(UpperCamelCase_							),
																			]:
																									_snake_case		:	Tuple							=       torch.zeros(
																									    self.model_tester.batch_size ,							dtype=torch.long ,							device=UpperCamelCase_							)

													return inputs_dict



							def 			UpperCamelCase			(		self							):
													self.config_tester.run_common_tests()



							@unittest.skip(reason="VideoMAE does not use inputs_embeds"							)
							def 			UpperCamelCase			(		self							):
													pass



							def 			UpperCamelCase			(		self							):
													_snake_case							,_snake_case		:	Dict							=       self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			_snake_case		:	List[Any]							=       model_class(UpperCamelCase_							)
																			self.assertIsInstance(model.get_input_embeddings() ,							(nn.Module)							)
																			_snake_case		:	Optional[Any]							=       model.get_output_embeddings()
																			self.assertTrue(x is None or isinstance(UpperCamelCase_ ,							nn.Linear							)							)



							def 			UpperCamelCase			(		self							):
													_snake_case							,_snake_case		:	List[Any]							=       self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			_snake_case		:	List[str]							=       model_class(UpperCamelCase_							)
																			_snake_case		:	str							=       inspect.signature(model.forward							)
																			# signature.parameters is an OrderedDict => so arg_names order is deterministic
																			_snake_case		:	Optional[Any]							=       [*signature.parameters.keys()]

																			_snake_case		:	Tuple							=       ["pixel_values"]
																			self.assertListEqual(arg_names[:1] ,							UpperCamelCase_							)



							def 			UpperCamelCase			(		self							):
													_snake_case		:	Any							=       self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_model(*UpperCamelCase_							)



							def 			UpperCamelCase			(		self							):
													_snake_case		:	Dict							=       self.model_tester.prepare_config_and_inputs()
													self.model_tester.create_and_check_for_pretraining(*UpperCamelCase_							)



							@slow
							def 			UpperCamelCase			(		self							):

													for model_name in VIDEOMAE_PRETRAINED_MODEL_ARCHIVE_LIST[:1]:
																			_snake_case		:	Any							=       VideoMAEModel.from_pretrained(UpperCamelCase_							)
																			self.assertIsNotNone(UpperCamelCase_							)



							def 			UpperCamelCase			(		self							):

													if not self.has_attentions:
																			pass

													else:
																			_snake_case							,_snake_case		:	List[str]							=       self.model_tester.prepare_config_and_inputs_for_common()
																			_snake_case		:	Dict							=       True

																			for model_class in self.all_model_classes:
																									_snake_case		:	Optional[int]							=       self.model_tester.seq_length - self.model_tester.num_masks
																									_snake_case		:	Union[str, Any]							=       (
																									    num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length
																									)

																									_snake_case		:	Optional[Any]							=       True
																									_snake_case		:	Dict							=       False
																									_snake_case		:	Optional[Any]							=       True
																									_snake_case		:	Union[str, Any]							=       model_class(UpperCamelCase_							)
																									model.to(UpperCamelCase_							)
																									model.eval()
																									with torch.no_grad():
																															_snake_case		:	int							=       model(**self._prepare_for_class(UpperCamelCase_ ,							UpperCamelCase_							)							)
																									_snake_case		:	Optional[int]							=       outputs.attentions
																									self.assertEqual(len(UpperCamelCase_							) ,							self.model_tester.num_hidden_layers							)

																									# check that output_attentions also work using config
																									del inputs_dict["output_attentions"]
																									_snake_case		:	Union[str, Any]							=       True
																									_snake_case		:	Dict							=       model_class(UpperCamelCase_							)
																									model.to(UpperCamelCase_							)
																									model.eval()
																									with torch.no_grad():
																															_snake_case		:	Optional[int]							=       model(**self._prepare_for_class(UpperCamelCase_ ,							UpperCamelCase_							)							)
																									_snake_case		:	Union[str, Any]							=       outputs.attentions
																									self.assertEqual(len(UpperCamelCase_							) ,							self.model_tester.num_hidden_layers							)

																									self.assertListEqual(
																									    list(attentions[0].shape[-3:]							) ,							[self.model_tester.num_attention_heads, seq_len, seq_len] ,							)
																									_snake_case		:	int							=       len(UpperCamelCase_							)

																									# Check attention is always last and order is fine
																									_snake_case		:	List[Any]							=       True
																									_snake_case		:	Dict							=       True
																									_snake_case		:	Tuple							=       model_class(UpperCamelCase_							)
																									model.to(UpperCamelCase_							)
																									model.eval()
																									with torch.no_grad():
																															_snake_case		:	Optional[Any]							=       model(**self._prepare_for_class(UpperCamelCase_ ,							UpperCamelCase_							)							)

																									self.assertEqual(out_len + 1 ,							len(UpperCamelCase_							)							)

																									_snake_case		:	Tuple							=       outputs.attentions

																									self.assertEqual(len(UpperCamelCase_							) ,							self.model_tester.num_hidden_layers							)
																									self.assertListEqual(
																									    list(self_attentions[0].shape[-3:]							) ,							[self.model_tester.num_attention_heads, seq_len, seq_len] ,							)



							def 			UpperCamelCase			(		self							):

													def check_hidden_states_output(lowercase_ ,							lowercase_ ,							lowercase_							):
																			_snake_case		:	List[str]							=       model_class(UpperCamelCase_							)
																			model.to(UpperCamelCase_							)
																			model.eval()

																			with torch.no_grad():
																									_snake_case		:	str							=       model(**self._prepare_for_class(UpperCamelCase_ ,							UpperCamelCase_							)							)

																			_snake_case		:	Optional[int]							=       outputs.hidden_states
																			_snake_case		:	str							=       self.model_tester.num_hidden_layers + 1
																			self.assertEqual(len(UpperCamelCase_							) ,							UpperCamelCase_							)

																			_snake_case		:	Optional[Any]							=       self.model_tester.seq_length - self.model_tester.num_masks
																			_snake_case		:	Union[str, Any]							=       num_visible_patches if model_class == VideoMAEForPreTraining else self.model_tester.seq_length

																			self.assertListEqual(
																			    list(hidden_states[0].shape[-2:]							) ,							[seq_length, self.model_tester.hidden_size] ,							)

													_snake_case							,_snake_case		:	List[Any]							=       self.model_tester.prepare_config_and_inputs_for_common()

													for model_class in self.all_model_classes:
																			_snake_case		:	Union[str, Any]							=       True
																			check_hidden_states_output(UpperCamelCase_ ,							UpperCamelCase_ ,							UpperCamelCase_							)

																			# check that output_hidden_states also work using config
																			del inputs_dict["output_hidden_states"]
																			_snake_case		:	Tuple							=       True

																			check_hidden_states_output(UpperCamelCase_ ,							UpperCamelCase_ ,							UpperCamelCase_							)



							@unittest.skip("Will be fixed soon by reducing the size of the model used for common tests."							)
							def 			UpperCamelCase			(		self							):
													pass




def    snake_case		()			->			Dict:

						'''simple docstring'''



						_snake_case		:	int							=       hf_hub_download(
						    repo_id="hf-internal-testing/spaghetti-video"   ,     filename="eating_spaghetti.npy"   ,     repo_type="dataset"		)
						_snake_case		:	str							=       np.load(__snake_case		)
						return list(__snake_case		)



@require_torch
@require_vision
class   lowercase_       (	unittest.TestCase						):



							@cached_property
							def 			UpperCamelCase			(		self							):
													return (
													    VideoMAEImageProcessor(image_mean=[0.5, 0.5, 0.5] ,							image_std=[0.5, 0.5, 0.5]							)
													    if is_vision_available()
													    else None
													)



							@slow
							def 			UpperCamelCase			(		self							):
													_snake_case		:	int							=       VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics"							).to(
													    UpperCamelCase_							)

													_snake_case		:	int							=       self.default_image_processor
													_snake_case		:	Union[str, Any]							=       prepare_video()
													_snake_case		:	Optional[int]							=       image_processor(UpperCamelCase_ ,							return_tensors="pt"							).to(UpperCamelCase_							)

													# forward pass
													with torch.no_grad():
																			_snake_case		:	Any							=       model(**UpperCamelCase_							)

													# verify the logits
													_snake_case		:	Optional[Any]							=       torch.Size((1, 400)							)
													self.assertEqual(outputs.logits.shape ,							UpperCamelCase_							)

													_snake_case		:	str							=       torch.tensor([0.3_669, -0.0_688, -0.2_421]							).to(UpperCamelCase_							)

													self.assertTrue(torch.allclose(outputs.logits[0, :3] ,							UpperCamelCase_ ,							atol=1e-4							)							)



							@slow
							def 			UpperCamelCase			(		self							):
													_snake_case		:	List[str]							=       VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short"							).to(UpperCamelCase_							)

													_snake_case		:	List[str]							=       self.default_image_processor
													_snake_case		:	Union[str, Any]							=       prepare_video()
													_snake_case		:	Tuple							=       image_processor(UpperCamelCase_ ,							return_tensors="pt"							).to(UpperCamelCase_							)

													# add boolean mask, indicating which patches to mask
													_snake_case		:	Dict							=       hf_hub_download(repo_id="hf-internal-testing/bool-masked-pos" ,							filename="bool_masked_pos.pt"							)
													_snake_case		:	Dict							=       torch.load(UpperCamelCase_							)

													# forward pass
													with torch.no_grad():
																			_snake_case		:	Dict							=       model(**UpperCamelCase_							)

													# verify the logits
													_snake_case		:	Dict							=       torch.Size([1, 1_408, 1_536]							)
													_snake_case		:	Optional[int]							=       torch.tensor(
													    [[0.7_994, 0.9_612, 0.8_508], [0.7_401, 0.8_958, 0.8_302], [0.5_862, 0.7_468, 0.7_325]] ,							device=UpperCamelCase_							)
													self.assertEqual(outputs.logits.shape ,							UpperCamelCase_							)
													self.assertTrue(torch.allclose(outputs.logits[0, :3, :3] ,							UpperCamelCase_ ,							atol=1e-4							)							)

													# verify the loss (`config.norm_pix_loss` = `True`)
													_snake_case		:	Any							=       torch.tensor([0.5_142] ,							device=UpperCamelCase_							)
													self.assertTrue(torch.allclose(outputs.loss ,							UpperCamelCase_ ,							atol=1e-4							)							)

													# verify the loss (`config.norm_pix_loss` = `False`)
													_snake_case		:	Tuple							=       VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base-short" ,							norm_pix_loss=UpperCamelCase_							).to(
													    UpperCamelCase_							)

													with torch.no_grad():
																			_snake_case		:	Union[str, Any]							=       model(**UpperCamelCase_							)

													_snake_case		:	str							=       torch.tensor(torch.tensor([0.6_469]							) ,							device=UpperCamelCase_							)
													self.assertTrue(torch.allclose(outputs.loss ,							UpperCamelCase_ ,							atol=1e-4							)							)







"""simple docstring"""
import argparse
import json
import os

from tensorflow.core.protobuf.saved_model_pba import SavedModel


# All paths are set with the intent you should run this script from the root of the repo with the command
# python utils/check_copies.py
snake_case : Optional[Any]     		=		"""."""

# Internal TensorFlow ops that can be safely ignored (mostly specific to a saved model)
snake_case : List[str]     		=		[
    """Assert""",
    """AssignVariableOp""",
    """EmptyTensorList""",
    """MergeV2Checkpoints""",
    """ReadVariableOp""",
    """ResourceGather""",
    """RestoreV2""",
    """SaveV2""",
    """ShardedFilename""",
    """StatefulPartitionedCall""",
    """StaticRegexFullMatch""",
    """VarHandleOp""",
]


def 							A						(				__snake_case: Optional[Any] ,     __snake_case: Tuple ,     __snake_case: Any  )    ->		int:

       """simple docstring"""





       __magic_name__      				=    SavedModel()
       __magic_name__      				=    []

       with open(os.path.join(__snake_case ,     'utils' ,     'tf_ops' ,     'onnx.json'  )  ) as f:
              __magic_name__      				=    json.load(__snake_case  )['opsets']

       for i in range(1 ,     opset + 1  ):
              onnx_ops.extend(onnx_opsets[str(__snake_case  )]  )

       with open(__snake_case ,     'rb'  ) as f:
              saved_model.ParseFromString(f.read()  )

       __magic_name__      				=    set()

       # Iterate over every metagraph in case there is more than one (a saved model can contain multiple graphs)
       for meta_graph in saved_model.meta_graphs:
              # Add operations in the graph definition
              model_op_names.update(node.op for node in meta_graph.graph_def.node  )

              # Go through the functions in the graph definition
              for func in meta_graph.graph_def.library.function:
                     # Add operations in each function
                     model_op_names.update(node.op for node in func.node_def  )

    # Convert to list, sorted if you want
       __magic_name__      				=    sorted(__snake_case  )
       __magic_name__      				=    []

       for op in model_op_names:
              if op not in onnx_ops and op not in INTERNAL_OPS:
                     incompatible_ops.append(__snake_case  )

       if strict and len(__snake_case  ) > 0:
              raise Exception(F"""Found the following incompatible ops for the opset {opset}:\n""" + incompatible_ops  )
       elif len(__snake_case  ) > 0:
              print(F"""Found the following incompatible ops for the opset {opset}:"""  )
              print(*__snake_case ,     sep='\n'  )
       else:
              print(F"""The saved model {saved_model_path} can properly be converted with ONNX."""  )


if __name__ == "__main__":
 snake_case : Optional[Any]     		=		argparse.ArgumentParser()
 parser.add_argument("""--saved_model_path""", help="""Path of the saved model to check (the .pb file).""")
 parser.add_argument(
     """--opset""", default=1_2, type=int, help="""The ONNX opset against which the model has to be tested."""
 )
 parser.add_argument(
     """--framework""", choices=["""onnx"""], default="""onnx""", help="""Frameworks against which to test the saved model."""
 )
 parser.add_argument(
     """--strict""", action="""store_true""", help="""Whether make the checking strict (raise errors) or not (raise warnings)"""
 )
 snake_case : Any     		=		parser.parse_args()

 if args.framework == "onnx":
  onnx_compliancy(args.saved_model_path, args.strict, args.opset)